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Does your organisation have the right data skills to stay ahead?

Nick Evans, marketing practice director, Jaywing

8th August 2017

Succeeding in today’s rapidly changing digital world – where the boundaries between physical and digital have disappeared – requires a new set of rules. From adopting new technology that combines online and offline data to acquiring a new skillset to make sense of the rising tide of data.

Machine learning, data science and predictive analytics are the new and increasingly crucial complements to traditional marketing analysis and insight best practice. Businesses that are more technically-savvy and use the latest analytical and data science techniques will have a significant competitive advantage as they engage intelligently with their customers.

In order to maximise these opportunities, it is likely that organisations will need to recruit new talent, including people with specialised data management, analytics and machine learning knowledge. But finding talented people with the right data skills is not easy.

Jaywing’s new research shows that a data science skills shortage is the main barrier restricting businesses driving more value from data. Yet, if you’re not using data science skills to inform your marketing strategy, you could be behind the curve. Without data skills, brands are lagging behind in the use of data-driven marketing, with only 18% adopting advanced attribution techniques and over half (65%) implementing only basic or segment-based personalisation.

Competing with data science

Brands that make data science and analytics investments wisely will see measurably better results. They will be able to apply data science techniques to every area of the business, from improving individual customer relationships to understanding which macro-level marketing and media investments generate the optimum returns.

The surge in interest in data science is partly fuelled by the vast quantities of data produced today. But, on top of this, reduced data storage costs, a rise in the prevalence of open source software that facilitates large-scale data management, and very advanced analysis and modelling have helped data science to become a specialist discipline. Regardless of these data and technological advances, many organisations are struggling to keep pace with what, in comparison, are relatively modest data and analytical challenges.

Brands have masses of data and need professionals who can gather and organise it in meaningful ways, as well as analyse it to enable them to make smarter business decisions. That’s why, in the last few years, employers from every sector have been increasing their efforts to plug the data science skills gap.

Yet, there is still a notable shortage of skilled data experts in the marketplace. And, while academic institutions race to prepare and adapt enough courses with the right content, the potential of the data revolution is yet to be fully realised.

Filling the data skills gap

Despite best efforts, many educational and professional training schemes struggle to catch up with the industry’s demands, leaving organisations no choice but to look for new ways to acquire talent with these data skills.

Creating a holistic data infrastructure requires people with different skills, including technical, insight and analytical, predictive modelling, consulting, and reporting and visualisation experts. That means it’s expensive and difficult to gain a “fast start” when building these resources.

That’s where partnering with data experts and insourcing specialists can help. It presents businesses with the opportunity to accelerate their data opportunities by bringing in the right mix of skills at the right time to drive programmes of change, whether that’s with a business starting its data journey or one looking to fast-track new initiatives. This means that businesses can acquire the exact type of data skills when they require them and pay only for the period they need them for.

These resources work on-site as part of the business, integrating with incumbent technologies and processes, supplementing existing resource to enable fast-tracking of data development opportunities. Bringing specialist external data science talent into a business often sees accelerated learning for existing staff, reduced operating costs and rapid assimilation of commercial goals and objectives.

As data continues to proliferate, the challenges of managing it and extracting value will, too. New technologies are helping us keep pace and artificial intelligence in particular offers some exciting opportunities to let machines, rather than people, do some of the clever thinking. However, even with these emerging technologies, marketers will still need to be able to plug them in to legacy systems and use analytically-derived insight to stay ahead. By insourcing specialist resource today, brands can effectively plug the data skills gap.

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